The Impact of Oral Hypoglycemics and Statins on Outcomes in Myelodysplastic Syndromes
Notice bibliographique
Résumé
Abstract Background: Observational studies suggest an anti-neoplastic effect associated with statins, metformin, dipeptidyl peptidase-4 inhibitors (DPP4i), while sulfonylureas may have a neutral or detrimental effect. The aim of this study was to determine the impact of these medications in patients with myelodysplastic syndromes (MDS). Methods: A prospective Canadian national registry containing disease and patient-related characteristics enrolled patients with MDS between January 1, 2006 and December 31, 2019. The Ontario subset of the registry was linked to population-based health system administrative databases. The primary outcome was the impact of statin/oral hypoglycemic medication exposure on overall survival (OS). Cumulative medication exposure (in months) was evaluated from 1-year prior to registry enrollment to date of death or end of follow-up (March 31, 2020) as a time-varying covariate. Cox regression analysis controlling for comorbid disease burden (Aggregated Diagnosis Groups; ADG) and sociodemographic factors (age, sex, rurality, income quintile) examined the relationship between medication exposure and OS. Our secondary outcome was leukemia-free survival (LFS), in which cause-specific hazard model was used to evaluate the association between medication exposure and LFS, taking death as the competing event. Results: In total, 533 patients aged >66 years were included (395 lower-risk IPSS, 130 higher-risk IPSS). The median age was 76.0 years (IQR 72.0-81.0), 65.1% were male and 9% had secondary MDS. The mean follow-up was 2.6 years (SD+ 2.4). Starting one year prior to registry enrollment and until the end of follow-up, 49.3% used a statin, 18.9% used metformin, 9.0% used a sulfonylurea and 6.4% used a DPP4i. On univariate analysis, we identified an improved OS in the lower-risk IPSS group using DPP4i (HR 0.98, 95% CI 0.95-1.00, p=0.05), while there was no significant difference in the higher-risk IPSS group for users of DPP4i (HR 1.03, 95% CI 0.99-1.07, p=0.21). In both lower and higher-risk IPSS groups, there was no difference in mortality for statins (HR 1.00, CI 1.00-1.01, p=0.93), metformin (HR 1.00, CI 0.99-1.01, p=0.81) and sulfonylureas (HR 1.00, CI 0.99-1.02, p=0.43). Increased age (p<0.01), higher Charlson score (p<0.01), greater comorbid disease burden (p=0.04), higher-risk disease (IPSS p<0.01, IPSS-R p<0.01), history of heart failure (p<0.01) and hypertension (p=0.01) were predictive of inferior OS on univariate analysis. There was no association between exposure to the studied medications and LFS in the lower-risk group: metformin (HR 0.99, 95% CI 0.96-1.02, p=0.32), sulfonylureas (HR 0.99, 95% CI 0.94-1.04, p=0.57) and statins (HR 0.99, 95% CI 0.98-1.01, p=0.41). The impact of DPP4i exposure on LFS could not be assessed in lower risk disease due to infrequent events. There was also no association in the higher-risk IPSS group: DPP4i (HR 1.06, 95% CI 1.00-1.12, p=0.05), metformin (HR 1.02, CI 0.98-1.05, p=0.34), sulfonylureas (HR 1.04, 1.00-1.08, p=0.07) and statins (HR 1.02, 1.00-1.04, p=0.12). On multivariable analysis in the lower-risk IPSS group, no associations were identified between the use of medications of interest and all-cause mortality: DPP4i (HR 0.98, 95% CI 0.95-1.00, p=0.06), metformin (HR 1.00, 95% CI 0.99-1.01, p=0.99), sulfonylurea (HR 1.00, 95% CI 0.99-1.02, p=0.65) and statins (HR 1.00, 95% CI 0.99-1.00, p=0.56). In those with known cause of death, the main cause of death amongst DPP4i users was infection (35.7%) followed by acute myeloid leukemia (AML) (21.4%) and cardiac disease (14.3%), while the main cause of death in patients not on DPP4i was progressive MDS (23.2%) followed by AML (22.5%) and infections (22.1%). Conclusions: DPP4i may confer a survival benefit in lower-risk but not higher-risk MDS that cannot be explained by a reduction in cardiovascular deaths. Metformin, sulfonylureas and statins showed no impact on OS and LFS in lower and higher-risk groups. Improved bone marrow function through exosome inhibition and increased granulocyte-macrophage colony stimulating activity is the suggested mechanism for the potential survival benefit with DPP4i in lower-risk IPSS patients. This represents the first study evaluating the impact of oral hypoglycemic mediations and statins in a large cohort of patients with MDS. Further prospective studies are required to further evaluate the effects of DPP4i in MDS. Disclosures Leber: AMGEN: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Pfizer: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Abbvie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; TaiHo: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Otsuka: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Astellas: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Jazz: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Khalaf: Pfizer: Honoraria; Novartis: Honoraria; Paladin: Honoraria. Sabloff: TaiHo: Membership on an entity's Board of Directors or advisory committees; Astellas: Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Jaxx: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; ROCHE: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees. Yee: Astex: Membership on an entity's Board of Directors or advisory committees, Research Funding; Geron: Research Funding; Janssen: Research Funding; Jazz: Research Funding; MedImmune: Research Funding; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Onconova: Research Funding; Tolero: Research Funding; AbbVie: Honoraria; Bristol-Myers Squibb/Celgene: Membership on an entity's Board of Directors or advisory committees; Otsuka: Membership on an entity's Board of Directors or advisory committees; Paladin: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Shattuck Labs: Membership on an entity's Board of Directors or advisory committees; TaiHo: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Genentech: Research Funding; F. Hoffmann La Roche: Membership on an entity's Board of Directors or advisory committees, Research Funding; Forma Therapeutics: Research Funding. Buckstein: Celgene: Research Funding; BMS: Membership on an entity's Board of Directors or advisory committees; Takeda: Research Funding; TAIHO: Research Funding; Otsuka: Research Funding.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».